A Comparison of Computational Color Constancy Algorithms, Part 2;
Experiments with Images
Kobus Barnard, Lindsay Martin, Adam Coath, and Brian Funt,
"A Comparison of Computational Color Constancy Algorithms, Part 2;
Experiments with Images",
IEEE Transactions on Image Processing, Vol. 11, No. 9, pp 985-996, Sept
2002.
Abstract:
We test a number of the leading computational
color constancy algorithms using a comprehensive set of images.
These were of 33 different scenes under 11 different sources
representative of common illumination conditions. The algorithms
studied include two gray world methods, a version of the
Retinex method, several variants of Forsyth’s gamut-mapping
method, Cardei et al.’s neural net method, and Finlayson et al.’s
Color by Correlation method. We discuss a number of issues
in applying color constancy ideas to image data, and study
in depth the effect of different preprocessing strategies. We
compare the performance of the algorithms on image data with
their performance on synthesized data. All data used for this
study is available online at http://www.cs.sfu.ca/~color/data, and
implementations for most of the algorithms are also available
(http://www.cs.sfu.ca/~color/code).
Experiments with synthesized data (part one of this paper)
suggested that the methods which emphasize the use of the input
data statistics, specifically Color by Correlation and the neural
net algorithm, are potentially the most effective at estimating
the chromaticity of the scene illuminant. Unfortunately, we were
unable to realize comparable performance on real images. Here
exploiting pixel intensity proved to be more beneficial than
exploiting the details of image chromaticity statistics, and the
three-dimensional (3-D) gamut-mapping algorithms gave the best
performance.
Full text (pdf)
Keywords: Algorithm, color by correlation, color constancy,
comparison, computational, gamut constraint, neural network.
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